Authorship Attribution Research Articles

Authorship attribution, idiolectal style, and online identity

Assistant ProfessorDepartment of English LanguageCollege of Language SciencesKing Saud UniversityRiyadh 11495 Kingdom of Saudi Arabia Awarding Institution: University of Leeds, UKDate of Award: 14 October 2022

A cyberterrorist behind the keyboard

Abstract Given the diverse backgrounds of people living in modern societies as well as the international nature of cyber-terrorist threats, profiling the type of person behind cyber-mediated crimes has become a norm in terrorist profiling practice. This study contributes to timely efficient terrorist profiling and threat assessment by showcasing an automated content analysis of cyber-mediated terrorist texts, using natural language processing technology and AI-assisted analysis. To characterise the terrorist type of texts and provide clues to threats, the study employs a ‘psycholinguistic profiling’ approach to authorship analysis (Grant 2008). That is, it seeks to describe the likelihood of an author’s engagement in violent extremist activity, identify motives for violence, and provide clues vis-a-vis would-be and actual violent behaviours. The study takes twenty texts produced by international terrorists involved in jihadism and far-right violent extremism as a case study. The findings reveal the investigative value of automated psycholinguistic profiling for security and intelligence practitioners, with the semantic patterns yielding helpful information for an understanding of the criminal nature of terrorist language. Also revealed is the attentional pattern of extremists and their discourse together with clues-based conclusions about text type, as well as ‘warning’ behaviours and motives for aggression which vary according to the authors’ ideological differences.

TWO QUESTIONS: THE MASʾALATĀN AND THE AVICENNIAN CORPUS

AbstractThis article brings to light a previously unedited short treatise, the Masʾalatān (Two Questions), attributed to Avicenna (d. 1037). While the earliest witness to the text is the Ayasofya 4853 manuscript, containing a substantial portion of Avicenna's Nachlass, some of which is integrated into the Mubāḥaṯāt and Taʿlīqāt, the Masʾalatān has remained a standalone work with limited circulation. Consequently, the primary concern revolves around the verification of its authenticity and its feasibility given the available data. This article presents a critical edition of the text alongside a parallel translation but it also serves as a case study on the possibilities of authorship verification. It also compiles information from codicology, nevertheless, it primarily focuses on the commentary that analyses and compares the arguments to Avicenna's unquestionably authentic solutions. The first question addresses whether every existent is spatially located, while the second explores the impossibility of an actual infinite body. The commentary endeavors to interpret the text against the cultural and theological background that may have inspired such inquiries, meanwhile also seeks to address its later influence. In addition to unveiling a hitherto unseen text to the scholarly community for further research, it also offers an insight into the limitations of authorship attribution.

Examining self-described policy-relevant evidence base for policymaking: an evidence map of COVID-19 literature

BackgroundEvidence-based policymaking is a paradigm aimed at increasing the use of evidence by actors involved in policymaking processes. The COVID-19 pandemic highlighted a heavy reliance on emerging evidence for policymaking...

Enhancing Tourism Sustainability of Archipelago Countries: A Bibliometric Exploration of the Triple Helix Concept

This study employs a comprehensive bibliometric approach to unravel the intricate economic, ecological, and social linkages in sustainable tourism within archipelago countries. Utilizing bibliographic methodologies, the research endeavours to map the literature related to this specified theme, with Scopus serving as the primary data source. The VOS-viewer analysis tool is employed to delineate and characterize the identified keywords. A total of 1,380 documents related to the research theme were meticulously gathered, with the study specifically limiting the recorded country areas to selected archipelago countries. Authorship analysis spotlights Hall C.M. as the foremost contributor based on the analyzed sample data, and the literature journal "Governance, the State and Sustainable Tourism: A Political Economy Approach" emerges as the most-cited publication. The United Kingdom takes centre stage as the primary contributor among countries. At the same time, the School of Business and Economics at Linnaeus University, Sweden, stands out as the institution making the most substantial contribution to publications concerning the economic, ecological, and social linkages of sustainable tourism in archipelago countries. This study significantly contributes to the mapping of literature on economic, ecological, and social interconnections, providing valuable insights into sustainable tourism in archipelago countries over the past 12 years.

Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis.

The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.

Scientific misconduct responsibility attribution: An empirical study on byline position and team identity in Chinese medical papers.

The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed. Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures. First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures. Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

Women occupying preferential authorship positions for articles in the journal Emergencias: an analysis of authorship during the last decade

Women occupying preferential authorship positions for articles in the journal Emergencias: an analysis of authorship during the last decade

Frontier and hot topics in the application of hydrogel in the biomedical field: a bibliometric analysis based on CiteSpace

Hydrogels are formed of crosslinked polymer chains arranged in three-dimensional (3D) networks. These chains have good water-containing capacity and are soft and malleable. Hydrogels have good biocompatibility due to their significant water content, flexible structure, and numerous holes. These characteristics make them analogous to biological tissues. Despite the publication of 8700 literature related to hydrogel biomedical applications in the past 52 years (1973 ~ 2024), studies on the use of hydrogels in biomedicine are few. To gain a comprehensive understanding of their current development status, research trends, and prospects in the biomedical application field, it is imperative to conduct a thorough retrospective analysis. In this study, we employ bibliometric analysis and CiteSpace software to quantitatively and visually analyze articles published in this field. Firstly, we provide a quantitative analysis of authorship and institutional publications over the past 52 years to elucidate the fundamental development status regarding hydrogel biomedical applications. Secondly, we did visual studies on terms that are high-frequency, explosive, keyword clustering, and so on, to understand the directionality and evolution of the main research hotspots during each period. Notably, our findings emphasize that fabricating hydrogels into wound healing-promoting dressings emerges as a prominent hotspot within the application field. We anticipate that this paper will inspire researchers with novel ideas for advancing hydrogel applications in biomedicine.

A Transformer-based approach to highly granular source code authorship attribution

Traditional source code authorship identification methods often rely on features such as textual similarity, programming style or metadata, however, these methods often struggle to extract the precise source code authoring style when dealing with large-scale code bases or complex programming patterns, resulting in poor performance. Therefore, this paper proposes a Transformer-based high fine-grained source code author attribution method.Aiming at the problem of roughness of existing literature on word segmentation, this paper proposes a high fine-grained source code segmentation method to extract higher fine-grained features. Aiming at the problem of feature dimension redundancy, this paper adopts the Transformer network to locate sensitive features that can characterise the author's style.To verify the effectiveness of the model, it was tested on GCJ-C++ and GCJ-Java datasets. The experimental results show that the proposed method model achieves higher recognition accuracy in the source code authorship attribution problem compared to the traditional methods.

A Transformer-based approach to highly granular source code authorship attribution

Traditional source code authorship identification methods often rely on features such as textual similarity, programming style or metadata, however, these methods often struggle to extract the precise source code authoring style when dealing with large-scale code bases or complex programming patterns, resulting in poor performance. Therefore, this paper proposes a Transformer-based high fine-grained source code author attribution method.Aiming at the problem of roughness of existing literature on word segmentation, this paper proposes a high fine-grained source code segmentation method to extract higher fine-grained features. Aiming at the problem of feature dimension redundancy, this paper adopts the Transformer network to locate sensitive features that can characterise the author's style.To verify the effectiveness of the model, it was tested on GCJ-C++ and GCJ-Java datasets. The experimental results show that the proposed method model achieves higher recognition accuracy in the source code authorship attribution problem compared to the traditional methods.

Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?

Abstract Authorship verification is the task of determining if two distinct writing samples share the same author and is typically concerned with the attribution of written text. In this paper, we explore the attribution of transcribed speech, which poses novel challenges. The main challenge is that many stylistic features, such as punctuation and capitalization, are not informative in this setting. On the other hand, transcribed speech exhibits other patterns, such as filler words and backchannels (e.g., um, uh-huh), which may be characteristic of different speakers. We propose a new benchmark for speaker attribution focused on human-transcribed conversational speech transcripts. To limit spurious associations of speakers with topic, we employ both conversation prompts and speakers participating in the same conversation to construct verification trials of varying difficulties. We establish the state of the art on this new benchmark by comparing a suite of neural and non-neural baselines, finding that although written text attribution models achieve surprisingly good performance in certain settings, they perform markedly worse as conversational topic is increasingly controlled. We present analyses of the impact of transcription style on performance as well as the ability of fine-tuning on speech transcripts to improve performance.1

Authorship Analysis and the Authenticity of Euripides’ Electra 518–44: Preserving Character Consistency

Authorship Analysis and the Authenticity of Euripides’ <i>Electra</i> 518–44: Preserving Character Consistency

Compiler-provenance identification in obfuscated binaries using vision transformers

Extracting compiler-provenance-related information (e.g., the source of a compiler, its version, its optimization settings, and compiler-related functions) is crucial for binary-analysis tasks such as function fingerprinting, detecting code clones, and determining authorship attribution. However, the presence of obfuscation techniques has complicated the efforts to automate such extraction. In this paper, we propose an efficient and resilient approach to provenance identification in obfuscated binaries using advanced pre-trained computer-vision models. To achieve this, we transform the program binaries into images and apply a two-layer approach for compiler and optimization prediction. Extensive results from experiments performed on a large-scale dataset show that the proposed method can achieve an accuracy of over 98 % for both obfuscated and deobfuscated binaries.

Ugotavljanje avtorstva in materialnost zgodnjenovoveških dramskih besedil

The digitization of texts and the advent of big data analyses have transformed our understanding of authorship and collaboration in early modern drama. However, these advancements ought to be carefully contextualized within the material realities of early modern playwriting. The scarcity of surviving dramatic manuscripts underscores the significant role of agents like compositors, printers and editors, and the loss of the majority of plays from English commercial theatres casts doubt on the reliability of comparisons based on unique or common verbal parallels. The article focuses on drama from the Elizabethan and Jacobean periods, particularly the recently proposed collaboration between Christopher Marlowe and William Shakespeare on the Henry VI plays. Applying the IT concept of GIGO (garbage in, garbage out), it highlights the impact of textual transmission intricacies on authorship attribution, emphasizing that even the most sophisticated attribution techniques are only as reliable as the (often unreliable) data they utilize.

Mediumship and stylometry: Exploring a new way of attributing authorship to mediumistic writings

Stylometry is still largely unexplored for applications in attributing authorship to mediumistic writings, as outlined in the subsequent literature review. Stylometry is a statistical method for analyzing authorship with a history to detect anonymous authors, e. g., J. K. Rowling as the real author of The Cuckoo’s Calling. Modern stylometry transcends literature analysis, applying to forensic inquiries, such as plagiarism detection, online security, and group identity characteristics. Historically, stylometric research unfolds in three phases: focusing on single numerical functions, statistical methods based on word frequencies, and contemporary use of machine learning. Authorship analysis includes three primary objectives: authorship attribution, verification, and characterization. It uses mathematical linguistics, information theory, and linguistic features like vocabulary richness, word frequency, and syntactic structures. Stylometric methods reliably detect authorship in imitation scenarios (pastiche) and have now also led to adversarial stylometry, which facilitates and identifies attempts at anonymization. Distribution patterns of features such as function words or punctuation can only be detected with high computing power and appear to contain too little content or emotion to be recognized by psi abilities of mediums. Imitation on a stylometric level has not yet been achieved by professional writers and does not appear to be one of the abilities of savants either, whose left hemisphere, including speech and language, is the least developed, which is why such genuine talent cannot be assumed in mediums either. Due to its undeniable successes, its quantitative and computational approach, and its potential to investigate a wide range of research questions, stylometry therefore appears to be a desirable auxiliary science for mediumship research.

Reducing the Impact of Time Evolution on Source Code Authorship Attribution via Domain Adaptation

Source code authorship attribution is an important problem in practical applications such as plagiarism detection, software forensics, and copyright disputes. Recent studies show that existing methods for source code authorship attribution can be significantly affected by time evolution, leading to a decrease in attribution accuracy year by year. To alleviate the problem of Deep Learning (DL)-based source code authorship attribution degrading in accuracy due to time evolution, we propose a new framework called Time D omain A daptation (TimeDA) by adding new feature extractors to the original DL-based code attribution framework that enhances the learning ability of the original model on source domain features without requiring new or more source data. Moreover, we employ a centroid-based pseudo-labeling strategy using neighborhood clustering entropy for adaptive learning to improve the robustness of DL-based code authorship attribution. Experimental results show that TimeDA can significantly enhance the robustness of DL-based source code authorship attribution to time evolution, with an average improvement of 8.7% on the Java dataset and 5.2% on the C++ dataset. In addition, our TimeDA benefits from employing the centroid-based pseudo-labeling strategy, which significantly reduced the model training time by 87.3% compared to traditional unsupervised domain adaptive methods.

Explainable Authorship Identification in Cultural Heritage Applications

While a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability to explain the reasons behind their predictions. This substantially hinders the practical application of AId methods, since the predictions returned by such systems are hardly useful unless they are supported by suitable explanations. In this article, we explore the applicability of existing general-purpose eXplainable Artificial Intelligence (XAI) techniques to AId, with a focus on explanations addressed to scholars working in cultural heritage. In particular, we assess the relative merits of three different types of XAI techniques (feature ranking, probing, factual and counterfactual selection) on three different AId tasks (authorship attribution, authorship verification and same-authorship verification) by running experiments on real AId textual data. Our analysis shows that, while these techniques make important first steps towards XAI, more work remains to be done to provide tools that can be profitably integrated into the workflows of scholars.

Analyzing Security Risks in Browser Extension Search Tools: A Literature Review

The literature review titled "Analyzing Security Risks in Browser Extension Search Tools" explores the growing interconnection between the digital and physical domains and the vulnerabilities and threats that accompany it. The study highlights how the Darkweb can be used for communication and anonymity, but because it is encrypted, it presents difficulties for law enforcement. Although browser extensions are essential for improving user experiences when browsing, they also offer security vulnerabilities like cross-site scripting and privilege escalation that can be used maliciously. The study emphasizes the necessity of better detection techniques for malicious extensions using Static and dynamic analysis combined, as well as the significance of advanced threat analysis and AI integration to foresee and mitigate cyber attacks.In order to improve the practical applicability of security measures, the study also urges a deeper comprehension of user behaviors and socioeconomic dynamics inside the Darkweb ecosystem. In order to successfully handle evolving cyber risks, future research topics include developing automated security testing procedures, upgrading authorship attribution algorithms for user privacy, and developing tools such as the WAP static analysis tool. Keywords: Browser add-ons, security hazards, Darkweb, online threats, sophisticated threat analysis, artificial intelligence integration, methods of detection, user conduct, and acknowledgment of authors WAP tool, cybersecurity, and automated security testing.

Gift and ghost authorship and the use of authorship guidelines in psychology journals: A cross-sectional survey

More than 800 (co-)authors participated in a large-scale cross-sectional survey on inappropriate attribution of authorship and the use of explicit authorship guidelines in psychological science (response rate 29.6%, predominantly from Europe and North America). Almost half of the respondents had been involved in a study where someone was added as an author who did not contribute substantially (gift authorship) at least a few times. Being involved in a study where someone was not listed as an author when they contributed substantially (ghost authorship) was experienced considerably less frequently. In approximately half of the respondents’ research settings, the use of explicit authorship guidelines is actively encouraged, leading to more frequent discussion of authorship in earlier stages, as well as to the perception of authorship decisions as fairer. Encouraging the use of explicit authorship guidelines is a simple yet effective intervention. Importantly, the American Psychological Association’s (APA) authorship guidelines are considerably more lenient than the widely used criteria of the Committee on Publication Ethics and the International Committee of Medical Journal Editors.